Influence of Thermal Imagery Resolution on Accuracy of Deep Learning based Face Recognition

Human-system interactions frequently require a retrieval of the key context information about the user and the environment. Image processing techniques have been widely applied in this area, providing details about recognized objects, people and actions. Considering remote diagnostics solutions, e.g. non-contact vital signs estimation and smart home monitoring systems that utilize person's identity, security is a very important factor. Thus, thermal imaging has become more and more popular, as it does not reveal features that are often used for person recognition, i.e. sharp edges, clear changes of pixel values between areas, etc. On the other hand, there are much more visible light data available for deep model training. Taking it into account, person recognition from thermography is much more challenging due to specific characteristics (blurring and smooth representation of features) and small amount of training data. Moreover, when low resolution data is used, features become even less visible, so this problem may become more difficult. This study focuses on verifying whether model trained to extract important facial embedding from RGB images can perform equally well if applied to thermal domain, without additional re-training. We also perform a set of experiments aim at evaluating the influence of resolution degradation by down-scaling images on the recognition accuracy. In addition, we present deep super-resolution (SR) model that by enhancing donw-scaled images can improve results for data acquired in scenarios that simulate real-life environment, i.e. mimicking facial expressions and performing head motions. Preliminary results proved that in such cases SR helps to increase accuracy by 6.5% for data 8 times smaller than original images. It has also been shown that it is possible to accurately recognize even 40 volunteers using only 4 images per person as a reference embedding. Thus, the initial profiles can be easily created in a real time, what is an additional advantage considering a solution setup in a new environment.